UnicodeDecodeError for hello-world example of keras-tuner

When I copy the hello-world example of keras-tuner and run in the env (tensorflow2.1.0 and python 3.7) directly in jupyter notebook, I get the following error:

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xd5 in position 246: invalid continuation byte

Does it mean there is some issues about decoding mnist dataset ? I am not sure what to do next.

The detailed output of error is:

Search space summary
|-Default search space size: 4
num_layers (Int)
|-default: None
|-max_value: 20
|-min_value: 2
|-sampling: None
|-step: 1
units_0 (Int)
|-default: None
|-max_value: 512
|-min_value: 32
|-sampling: None
|-step: 32
units_1 (Int)
|-default: None
|-max_value: 512
|-min_value: 32
|-sampling: None
|-step: 32
learning_rate (Choice)
|-default: 0.01
|-ordered: True
|-values: [0.01, 0.001, 0.0001]
Train on 10000 samples, validate on 10000 samples
Epoch 1/3
 9728/10000 [============================>.] - ETA: 6:52 - loss: 2.3025 - accuracy: 0.06 - ETA: 41s - loss: 2.3024 - accuracy: 0.1063 - ETA: 20s - loss: 2.3023 - accuracy: 0.123 - ETA: 13s - loss: 2.3022 - accuracy: 0.120 - ETA: 10s - loss: 2.3020 - accuracy: 0.118 - ETA: 8s - loss: 2.3016 - accuracy: 0.116 - ETA: 7s - loss: 2.3010 - accuracy: 0.13 - ETA: 5s - loss: 2.2998 - accuracy: 0.14 - ETA: 5s - loss: 2.2990 - accuracy: 0.15 - ETA: 4s - loss: 2.2973 - accuracy: 0.16 - ETA: 4s - loss: 2.2948 - accuracy: 0.17 - ETA: 3s - loss: 2.2912 - accuracy: 0.17 - ETA: 3s - loss: 2.2857 - accuracy: 0.17 - ETA: 3s - loss: 2.2786 - accuracy: 0.17 - ETA: 2s - loss: 2.2694 - accuracy: 0.18 - ETA: 2s - loss: 2.2569 - accuracy: 0.18 - ETA: 2s - loss: 2.2410 - accuracy: 0.18 - ETA: 2s - loss: 2.2215 - accuracy: 0.18 - ETA: 2s - loss: 2.2035 - accuracy: 0.19 - ETA: 1s - loss: 2.1845 - accuracy: 0.20 - ETA: 1s - loss: 2.1661 - accuracy: 0.20 - ETA: 1s - loss: 2.1501 - accuracy: 0.21 - ETA: 1s - loss: 2.1369 - accuracy: 0.21 - ETA: 1s - loss: 2.1220 - accuracy: 0.21 - ETA: 1s - loss: 2.1086 - accuracy: 0.21 - ETA: 0s - loss: 2.0918 - accuracy: 0.22 - ETA: 0s - loss: 2.0780 - accuracy: 0.22 - ETA: 0s - loss: 2.0627 - accuracy: 0.23 - ETA: 0s - loss: 2.0492 - accuracy: 0.23 - ETA: 0s - loss: 2.0354 - accuracy: 0.24 - ETA: 0s - loss: 2.0236 - accuracy: 0.24 - ETA: 0s - loss: 2.0121 - accuracy: 0.24 - ETA: 0s - loss: 2.0014 - accuracy: 0.2521
---------------------------------------------------------------------------
_FallbackException                        Traceback (most recent call last)
C:\WWW\anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\ops\gen_io_ops.py in save_v2(prefix, tensor_names, shape_and_slices, tensors, name)
   1700         _ctx._context_handle, tld.device_name, "SaveV2", name,
-> 1701         tld.op_callbacks, prefix, tensor_names, shape_and_slices, tensors)
   1702       return _result

_FallbackException: This function does not handle the case of the path where all inputs are not already EagerTensors.

During handling of the above exception, another exception occurred:

UnicodeDecodeError                        Traceback (most recent call last)
<ipython-input-3-cd4a152dd45c> in <module>
     33              y=y,
     34              epochs=3,
---> 35              validation_data=(val_x, val_y))
     36 
     37 tuner.results_summary()

C:\WWW\anaconda3\envs\tf2\lib\site-packages\kerastuner\engine\base_tuner.py in search(self, *fit_args, **fit_kwargs)
    128 
    129             self.on_trial_begin(trial)
--> 130             self.run_trial(trial, *fit_args, **fit_kwargs)
    131             self.on_trial_end(trial)
    132         self.on_search_end()

C:\WWW\anaconda3\envs\tf2\lib\site-packages\kerastuner\engine\multi_execution_tuner.py in run_trial(self, trial, *fit_args, **fit_kwargs)
     94 
     95             model = self.hypermodel.build(trial.hyperparameters)
---> 96             history = model.fit(*fit_args, **copied_fit_kwargs)
     97             for metric, epoch_values in history.history.items():
     98                 if self.oracle.objective.direction == 'min':

C:\WWW\anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    817         max_queue_size=max_queue_size,
    818         workers=workers,
--> 819         use_multiprocessing=use_multiprocessing)
    820 
    821   def evaluate(self,

C:\WWW\anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    395                       total_epochs=1)
    396                   cbks.make_logs(model, epoch_logs, eval_result, ModeKeys.TEST,
--> 397                                  prefix='val_')
    398 
    399     return model.history

C:\WWW\anaconda3\envs\tf2\lib\contextlib.py in __exit__(self, type, value, traceback)
    117         if type is None:
    118             try:
--> 119                 next(self.gen)
    120             except StopIteration:
    121                 return False

C:\WWW\anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in on_epoch(self, epoch, mode)
    769       if mode == ModeKeys.TRAIN:
    770         # Epochs only apply to `fit`.
--> 771         self.callbacks.on_epoch_end(epoch, epoch_logs)
    772       self.progbar.on_epoch_end(epoch, epoch_logs)
    773 

C:\WWW\anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\callbacks.py in on_epoch_end(self, epoch, logs)
    300     logs = logs or {}
    301     for callback in self.callbacks:
--> 302       callback.on_epoch_end(epoch, logs)
    303 
    304   def on_train_batch_begin(self, batch, logs=None):

C:\WWW\anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\callbacks.py in on_epoch_end(self, epoch, logs)
    990           self._save_model(epoch=epoch, logs=logs)
    991       else:
--> 992         self._save_model(epoch=epoch, logs=logs)
    993     if self.model._in_multi_worker_mode():
    994       # For multi-worker training, back up the weights and current training

C:\WWW\anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\callbacks.py in _save_model(self, epoch, logs)
   1025               self.best = current
   1026               if self.save_weights_only:
-> 1027                 self.model.save_weights(filepath, overwrite=True)
   1028               else:
   1029                 self.model.save(filepath, overwrite=True)

C:\WWW\anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\engine\network.py in save_weights(self, filepath, overwrite, save_format)
   1121              'saved.\n\nConsider using a TensorFlow optimizer from `tf.train`.')
   1122             % (optimizer,))
-> 1123       self._trackable_saver.save(filepath, session=session)
   1124       # Record this checkpoint so it's visible from tf.train.latest_checkpoint.
   1125       checkpoint_management.update_checkpoint_state_internal(

C:\WWW\anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\training\tracking\util.py in save(self, file_prefix, checkpoint_number, session)
   1166     file_io.recursive_create_dir(os.path.dirname(file_prefix))
   1167     save_path, new_feed_additions = self._save_cached_when_graph_building(
-> 1168         file_prefix=file_prefix_tensor, object_graph_tensor=object_graph_tensor)
   1169     if new_feed_additions:
   1170       feed_dict.update(new_feed_additions)

C:\WWW\anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\training\tracking\util.py in _save_cached_when_graph_building(self, file_prefix, object_graph_tensor)
   1114         or context.executing_eagerly() or ops.inside_function()):
   1115       saver = functional_saver.MultiDeviceSaver(named_saveable_objects)
-> 1116       save_op = saver.save(file_prefix)
   1117       with ops.device("/cpu:0"):
   1118         with ops.control_dependencies([save_op]):

C:\WWW\anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\training\saving\functional_saver.py in save(self, file_prefix)
    228         # _SingleDeviceSaver will use the CPU device when necessary, but initial
    229         # read operations should be placed on the SaveableObject's device.
--> 230         sharded_saves.append(saver.save(shard_prefix))
    231 
    232     with ops.control_dependencies(sharded_saves):

C:\WWW\anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\training\saving\functional_saver.py in save(self, file_prefix)
     70         tensor_slices.append(spec.slice_spec)
     71     with ops.device("cpu:0"):
---> 72       return io_ops.save_v2(file_prefix, tensor_names, tensor_slices, tensors)
     73 
     74   def restore(self, file_prefix):

C:\WWW\anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\ops\gen_io_ops.py in save_v2(prefix, tensor_names, shape_and_slices, tensors, name)
   1705         return save_v2_eager_fallback(
   1706             prefix, tensor_names, shape_and_slices, tensors, name=name,
-> 1707             ctx=_ctx)
   1708       except _core._SymbolicException:
   1709         pass  # Add nodes to the TensorFlow graph.

C:\WWW\anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\ops\gen_io_ops.py in save_v2_eager_fallback(prefix, tensor_names, shape_and_slices, tensors, name, ctx)
   1727   _attrs = ("dtypes", _attr_dtypes)
   1728   _result = _execute.execute(b"SaveV2", 0, inputs=_inputs_flat, attrs=_attrs,
-> 1729                              ctx=ctx, name=name)
   1730   _result = None
   1731   return _result

C:\WWW\anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     59     tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,
     60                                                op_name, inputs, attrs,
---> 61                                                num_outputs)
     62   except core._NotOkStatusException as e:
     63     if name is not None:

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xd5 in position 246: invalid continuation byte

[chr6192]

Yeah, I have found the problem. Too long a filename (path) causes the compilation failed. I got the same error when I give model.save_weights() a long path.

[shaoxiang777]

Yeah, I have found the problem. Too long a filename (path) causes the compilation failed. I got the same error when I give model.save_weights() a long path. How do you solve this? Even I give a new shorter name. It doesn’t work for me.